Generative adversarial networks (GANs) have attracted attention (see, e.g., [1]}, [2]}, [3]}, [4]}) with the development of real-world applications [5]}, [6]}, [7]}, [8]}. The generator network in GANs constructs synthetic data from random variables, and the discriminator network separates the synthetic data from the real-world data. One way of training GANs is to solve a Nash equilibrium problem [9]}, [10]} with two players, a discriminator that minimizes the synthetic-real discrimination error and a generator that maximizes this error.
